• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.897-904
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• 2022

Sports video is a very critical information resource. High-precision extraction of effective segments in sports video can better assist coaches in analyzing the player's actions in the video, and enable users to more intuitively appreciate the player's hitting action. Aiming at the shortcomings of the current sports video clip extraction results, such as strong subjectivity, large workload and low efficiency, a classification method of sports video clips based on MobileNetV3 is proposed to save user time. Experiments evaluate the effectiveness of effective segment extraction. Among the extracted segments, the effective proportion is 97.0%, indicating that the effective segment extraction results are good, and it can lay the foundation for the construction of the subsequent badminton action metadata video dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.670-672
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• 2021

색소 세포성 모반과 악성 흑색종은 형태가 유사하지만 유해성의 측면에서 악성 흑색종은 암으로써 무해한 색소 세포성 모반에 비해 위험한 질환이다. 이에 기반하여 기존 연구에서 색소 세포성 모반과 악성 흑색종을 구분하기 위한 연구가 있었지만, 데이터를 취득하는 과정에서 많은 cost 가 필요하였다. 본 연구에서는 이를 개선하기 위해 두 병변의 dermatoscopic 영상을 분류 학습의 데이터로 사용하여 연구를 진행하였다. 학습을 위한 데이터는 오픈소스 dermatoscopic 데이터셋인 HAM10000을 사용하였으며 모델은 CNN 에서 개선된 MobileNetV2 를 사용하였다. 실험 결과, MobileNetV2 를 사용한 학습은 3-layer CNN 에 비해 15 분의 1 가량 적은 파라미터를 가졌으며, 검증 성능과 테스트 성능에서 93%에 근사하는 성능을 보였다. 본 연구는 이전 연구에 비해 cost 측면에서 큰 개선을 이루었으며, 상용화 가능한 분류 기법을 발견했다는 점을 시사한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.236-240
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• 2008

People use diary or photograph for recall-ing their memory in order to satisfy their desires for recording their lives. If the experienced events are organized to a story, S/he can share her/his experience with others, and recall her/his significant events easily. In this paper, we propose a method that generates a story with Petri net and user contexts collected from mobile device. Here, we use Petri-net as a representation method that links human activities or experience causally. It is appropriate solution for modeling parallel events in real world, and for representing non-linear story line. In order to show the usefulness of the proposed method, we show an example of generating a story of user's experience with user contexts from mobile device and evaluate them.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.154-156
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• 2021

Depth-wise separable convolution 은 컴퓨터 자원이 제한된 환경에서 기존의 standard convolution을 대체하는데 강력하고, 효과적인 대안으로 잘 알려져 있다.[1] MobileNetV2 에서는 Inverted residual block을 소개한다. 이는 depth-wise separable convolution으로 인해 생기는 손실, 즉 channel 간의 데이터를 조합해 새로운 feature를 만들어낼 기회를 잃어버릴 때, 이를 depth-wise separable convolution 양단에 point-wise convolution(1×1 convolution)을 사용함으로써 극복해낸 block이다.[1] 하지만 1×1 convolution은 채널 수에 의존적(dependent)인 특징을 갖고 있고, 따라서 결국 네트워크가 깊어지면 깊어질수록 효율적이고(efficient) 가벼운(light weight) 네트워크를 만드는데 병목 현상(bottleneck)을 일으키고 만다. 이 논문에서는 channel-wise squeeze and excitation block(CSE)을 통해 1×1 convolution을 부분적으로 대체하는 방법을 통해 이 병목 현상을 해결한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.504-506
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• 2022

최근 인공지능 기술의 발달에 따라 여러 분야에 인공지능 기술이 활발히 응용되고 있다. 그중 안전 관리 분야에서 사람 인식을 통한 안전 관리 시스템의 지속적인 개발이 요구되고 있다. 그러나 실내 한정된 공간에서 사람들의 밀집도가 높은 경우 오브젝트의 중복도가 높아져 인식 성능이 낮아질 수 있다. 이를 해결하기 위해 본 논문은 사람의 밀집도가 높은 실내 환경에서 기존 객체 인식 기법의 성능을 분석하였다. 그리고 이러한 제한적인 환경에서 최적의 좋은 성능을 보일 수 있는 SSDLite와 MobileNetV2 모델을 기반으로 soft-NMS 기법을 적용하여 성능을 분석하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.490-493
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• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.570-590
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• 2024

Breast cancer ranks among the most prevalent forms of malignancy and foremost cause of death by cancer worldwide. It is not preventable. Early and precise detection is the only remedy for lowering the rate of mortality and improving the probability of survival for victims. In contrast to present procedures, thermography aids in the early diagnosis of cancer and thereby saves lives. But the accuracy experiences detrimental impact by low sensitivity for small and deep tumours and the subjectivity by physicians in interpreting the images. Employing deep learning approaches for cancer detection can enhance the efficacy. This study explored the utilization of thermography in early identification of breast cancer with the use of a publicly released dataset known as the DMR-IR dataset. For this purpose, we employed a novel approach that entails the utilization of a pre-trained MobileNetV2 model and fine tuning it through transfer learning techniques. We created three models using MobileNetV2: one was a baseline transfer learning model with weights trained from ImageNet dataset, the second was a fine-tuned model with an adaptive learning rate, and the third utilized early stopping with callbacks during fine-tuning. The results showed that the proposed methods achieved average accuracy rates of 85.15%, 95.19%, and 98.69%, respectively, with various performance indicators such as precision, sensitivity and specificity also being investigated.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.4
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• pp.121-127
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• 2011

Many peer-to-peer (p2p) systems have been studied in distributed, ubiquitous computing environments. Distributed hash table (DHT)-based p2p systems can improve load-balancing even though locality utilization and user mobility are not guaranteed. We propose a mobile locality-based hierarchical p2p overlay network (MLH-Net) to address locality problems without any other services. MLH-Net utilizes mobility features in a mobile environment. MLH-Net is constructed as two layers, an upper layer formed with super-nodes and a lower layer formed with normal-nodes. Because super-nodes can share advertisements, we can guarantee physical locality utilization between a requestor and a target during any discovery process. To overcome a node failure, we propose a simple recovery mechanism. The simulation results demonstrate that MLH-Net can decrease discovery routing hops by 15% compared with JXTA and 66% compared with Chord.

• Journal of Korea Multimedia Society
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• v.11 no.10
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• pp.1436-1445
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• 2008

Currently, mobile handset embeds many communication modules including CDMA and Bluetooth, and many applications are developed based on these modules. In this paper, we study about wireless vehicle diagnosis software and user interface based on bluetooth system on mobile handset. We developed Bluetooth communication system on protocol converter between OBD(On Board Diagnostics)-II system. Based on this system, we can communicate ECU(Engine Control Unit) and mobile device based on windows .NET compact framework platform. Therefore we can easily diagnose vehicle state and obtain engine data. All user interface and vehicle diagnosis systems on mobile handset are developed under windows .NET compact framework platform. Using this system we achieved several improvements over existing vehicle diagnostic system; 1) the software download and upgrade can be achieve on wireless environment, 2) no additional diagnostic devices are requires, which saves additional cost and we can diagnose the vehicle easily, 3) we can easily port our system on many embedded systems including PDA and navigator, etc.